This invention relates to an imaging system and related apparatus. An imaging system is one which has the ability to classify, identify, describe and/or analyze images.
An imaging system can be conceptualized as consisting of three components: an image acquisition component, a memory, and an inferential component that uses the memory to produce an interpretation of the image. Although the image acquisition component is generally straight forward in design and implementation, it has been exceeding difficult to synthesize systems of memory and inference that result in a useful imaging system. Despite a broad range of approaches to the solution of these problems, ranging from simple template matching to main stream artificial intelligence techniques, no imaging system has been devised that can approach the capabilities of the human vision system.
A pattern discrimination method is disclosed in U.S. Pat. No. 4,451,929 issued to Hajime Yoshida. In this patent, if the difference between the data from an object to be inspected and previously memorized data of a standard subject falls within a predetermined value, the object belongs to the same kind of standard subject. A similar U.S. Pat. No. 4,449,240 was issued to the same inventor.
In U.S. Pat. No. 4,521,909 issued to Patrick S. Wang, a pattern recognition system having both coarse and fine levels of analysis, is used to identify a work piece pattern. This pattern recognition system has a learning system in which a representation of a work piece pattern maybe incorporated into the memory.
U.S. Pat. No. 3,873,972 issued to Theodore H. Levine, relates to a character recognition system based on sequence characterization. This system also contains a learning mode.
In a patent issued to Bruce S. Buckley, U.S. Pat. No. 4,479,241, an automatic pattern recognition system is disclosed mimicking the neuron signal from the human brain.
U.S. Pat. No. 4,541,115 issued to Larry J. Werth, discloses a pattern processing system based on address sequencing. This system stores the image in the form of a binary pattern.
The need to create computer vision technology that can learn to interpret images from experience is a concept emphasized by R. M. Haralick and J. R. Ullmann, in "A Report on the 1982 IEEE Computer Vision Workshop" Pros. Workshop on Computer Vision, Representation and Control, VII-XIII, (1984).
Additional references are:
D. Marr, Vision, W. H. Freeman and Co., New York (1982). PA0 H. G. Barrow and J. M. Tenenbaum, "Computational Vision", in Proc IEEE 69, pp. 572-595 (1981). PA0 A. R. Hanson and E. M. Riseman, "VISIONS: A Computer System for Interpreting Scenes", in Computer Vision Systems, A. R. Hanson and E. M. Riseman, eds., Academic Press, New York (1978). PA0 R. A. Brooks, R. Greiner, and T. 0. Binford, "The ACRONYM Model-Based Vision System", in Proc. OJCAI 6, Tokyo, pp. 105-113 (1979). PA0 A. Rosenfeld, "Parallel Image Processing Using Cellular Arrays", IEEE Comput. Mag., 14-20 (1983). PA0 Winston, Henry and Horn, Berthold Klaus Paul, LISP, Addison-Wesley Publishing Co., 1981. PA0 Foderaro, Solink, and Sklower, Keith L., The FRANZ LISP MANUAL, University of California, 1981. PA0 Multisensor Data Fusion Involving Imagery, 14th Workshop on Applied Imagery Pattern Recognition; Oct. 3-4, 1985; IEEE Computer Society.